Multi-dimensional hybrid computing method, system, and corresponding equipment and media for enterprise data

Abstract

The present application discloses a multi-dimensional hybrid calculation method, system, corresponding equipment and medium for enterprise data, wherein the method includes: selecting data sets to be mutually calculated according to an analysis scenario; determining whether the dimensions of the data set with the least dimensions in the selected data set are all contained in any other data sets of the selected data set; determining one of the selected data sets as the first data set to determine the output dimension according to the dimension of the first data set; uniformly sorting the data items under each data set dimension in the selected data set; starting from the first data set, calculating a numerical column of the selected data set in sequence according to the data size order of the data set and a predetermined operation method; outputting the calculation result of the corresponding analysis scenario according to the dimension of the first data set and the calculated numerical column. The present invention can perform multi-dimensional hybrid calculations and minimize the amount of calculation without paying attention to whether the indicator dimensions are the same.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to a method for multi-dimensional hybrid computing of enterprise data. The present application also relates to a multi-dimensional hybrid computing system for enterprise data, and corresponding computer equipment and computer-readable storage media. Background Art

[0002] As the requirements for refined hospital management continue to increase, business scenarios are becoming increasingly complex, and operational analysis must meet multi-dimensional and multi-role requirements. For group hospitals and hospitals managed by multiple campuses, the analytical level requires an overall market analysis, and then penetrates the campus, department, diagnosis and treatment group, ward, individual, disease type, medical service items, etc. Due to the different characteristics of the business, the granularity of attention is becoming more and more refined, and the requirements for the dimensions and performance of data processing are also becoming higher and higher. De-dimensionalized calculations are not highly coupled with the business, and are time-consuming and labor-intensive with massive amounts of data. If dimensions and values are mixed in calculations, it is necessary to identify whether the indicator dimensions are the same. Only calculations with the same dimensions can be performed. Summary of the Invention

[0003] In order to overcome the deficiencies in the prior art, the present invention provides a method, system, and corresponding equipment and medium for multi-dimensional hybrid computing of enterprise data, which can perform multi-dimensional hybrid computing and minimize the amount of computing without paying attention to whether the indicator dimensions are the same.

[0004] In a first aspect of the present invention, a method for multi-dimensional hybrid computing of enterprise data is provided, comprising:

[0005] Select the data sets to be calculated based on the analysis scenario;

[0006] Determine whether all dimensions of the least dimensional dataset among the selected datasets are contained in any remaining datasets in the selected datasets;

[0007] In response to dimensions of the least dimensional dataset among the selected datasets being all contained in any remaining datasets of the selected datasets, determining one of the selected datasets as a first dataset to determine output dimensions according to the dimensions of the first dataset;

[0008] Sort the data items under each dataset dimension in the selected dataset in a unified manner;

[0009] Starting from the first data set, according to the order of data size of the data sets and the predetermined operation method, the calculation of a numerical column of the selected data set is performed in sequence;

[0010] The calculation results of the corresponding analysis scenario are output according to the dimensions of the first data set and the calculated numerical columns.

[0011] In a second aspect of the present invention, a multi-dimensional hybrid computing system for enterprise data is provided, comprising:

[0012] The selection module is used to select the data sets to be calculated based on the analysis scenario;

[0013] a first determining module, configured to determine whether all dimensions of the dataset with the least dimensions among the selected datasets are contained in any remaining datasets of the selected datasets;

[0014] a second determining module configured to, in response to dimensions of the dataset with the least dimensions among the selected datasets being all included in any remaining datasets of the selected datasets, determine one of the selected datasets as a first dataset to determine output dimensions according to the dimensions of the first dataset;

[0015] The sorting module is used to uniformly sort the data items under each dataset dimension in the selected dataset;

[0016] a calculation module, configured to calculate a numerical column of the selected data set in sequence starting from the first data set and according to the order of data size of the data sets and a predetermined calculation method;

[0017] The output module is used to output the calculation results of the corresponding analysis scenario according to the dimensions of the first data set and the calculated numerical columns.

[0018] In a third aspect of the present invention, a computer device is provided, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect of the present invention or implements the functions of the system according to the second aspect of the present invention when executing the computer program.

[0019] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method according to the first aspect of the present invention or implements the functions of the system according to the second aspect of the present invention.

[0020] According to the present invention, by selecting data sets to be calculated with each other according to the analysis scenario, and on the basis of determining that the selected data sets can be calculated, one of the selected data sets is determined as the first data set and the data items under each data set dimension in the selected data set are uniformly sorted respectively, and then, starting from the first data set, a numerical column of the selected data set is calculated in sequence according to the data size order of the data sets and a predetermined operation method, and finally, the calculation result of the corresponding analysis scenario is output according to the dimension of the first data set and the calculated numerical column, which solves the problem of uncertain output dimension due to uneven data set dimensions and avoids dimensional calculation at the same time, so there is no need to pay attention to whether the indicator dimensions are the same, and multi-dimensional mixed calculation can be performed and the amount of calculation is minimized, thereby ensuring the performance of data processing.

[0021] Other features and advantages of the present invention will become more apparent after reading the detailed description of the embodiments of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of an embodiment of a method according to the present invention;

[0023] Figure 2 FIG. 1 is a block diagram of an embodiment of a system according to the present invention.

[0024] The figures are schematic and simplified for clarity, and they merely show details which are essential to the understanding of the invention, while other details are left out. DETAILED DESCRIPTION

[0025] The following describes in detail the embodiments and examples of the present invention with reference to the accompanying drawings.

[0026] The scope of applicability of the present invention will become apparent from the detailed description given below. However, it should be understood that while the detailed description and specific examples indicate preferred embodiments of the invention, they are given for illustration purposes only.

[0027] Figure 1 A flowchart of a preferred embodiment of the enterprise data multi-dimensional hybrid computing method according to the present invention is shown.

[0028] In step S102, data sets to be calculated are selected according to the analysis scenario.

[0029] Based on the analysis scenario identified, such as outputting employee point values, existing datasets that may be involved in the analysis scenario can be listed for selection. The datasets can be pre-generated based on the specific circumstances of an enterprise, such as a hospital, in the following manner:

[0030] 1. Abstract and define data display dimensions based on the company's existing data. For example, you can customize analysis dimensions based on existing data and analysis scenarios. For hospitals, if the data is granularized down to the individual, you can define five dimensions based on the individual's health category: medical, nursing, technical, pharmaceutical, and management. Similarly, if departmental analysis is required, you can define four more dimensions: clinical, medical technology, medical support, and management, and so on.

[0031] 2. Identify the dimensions abstracted from the existing data. For example, the abstracted dimensions include: hospital district, department, treatment group, and individual. Label the entities according to their business scope, using B, D (D1, D2, D3, D4), U, and P (P1, P2, P3, P4, P5). D1, D2, D3, and D4 represent the four dimensions of clinical, medical technology, medical support, and management, while P1, P2, P3, P4, and P5 represent the five dimensions of medical, nursing, technology, pharmacy, and management.

[0032] 3. Define dimension sorting rules. Multiple rules can be used. For example, different rules can satisfy different analysis scenarios or themes, adhering to the principles of penetration and backtracking. A penetration rule is logically closed, with the default backtracking path being the inverse of the penetration path. This can also be matched with other penetration and backtracking rules. For example, if a rule is defined as "Campus-Department-Individual," the default backtracking rule for this rule is "Individual-Department-Campus." This rule can match both the penetration rule for "Campus-Individual" and the backtracking rule for "Individual-Campus-Department." This allows users to freely switch their focus without having to follow a rigid, step-by-step procedure. The dimension sorting rules determine the implementation path for requirements. For example, for the sorting rule "Individual-Department-Campus," since they are sequentially included, data calculations are aggregated, from individuals to departments, and then to the campus. However, for the sorting rule "Department-Campus-Individual," data from departments to campuses can be aggregated, but data from campuses to individuals must be split.

[0033] 4. Summarize the data value at the intersection of the dimensions (one and only one). For example, at a certain moment in a time dimension, the billing income of a doctor in the orthopedics department of this hospital has only one fixed value.

[0034] 5. The aggregation results of different dimensions are formed into data sets. Each data set contains at least one dimension and at least one value column. The dimension positions of different data sets are sorted according to one or more predefined dimension sorting rules.

[0035] For example, for an analysis scenario in which the relative value data of employee workload (employee point values) is calculated, the selected datasets may be the department coefficient dataset and the employee workload dataset, as shown below.

[0036]

[0037]

[0038] Department coefficient data set

[0039]

[0040]

[0041] Employee workload dataset

[0042] In step S104 , it is determined whether the dimensions of the dataset with the least dimensions in the selected dataset are all contained in any other datasets of the selected dataset. If so, the selected dataset can be used for subsequent calculations, and the processing proceeds to step S106 . Otherwise, the processing proceeds to step S120 .

[0043] For example, the selected data sets are N, M, and F. The data set N[a, b, c, n] has three dimensions a, b, and c. The data set M[a, b, c, m] has three dimensions a, b, and c. The data set F[a, b, c, d, …, m] has four dimensions a, b, c, and d. The data sets with the least dimensions are N[] and M[]. Since the data sets M[] and F[] both contain the dimensions of the data set N[], it can be considered that all the dimensions of the data set N[] are contained in any of the data sets M[] and F[]. Specifically, in the above analysis scenario of calculating the relative value data of employee workload, the department coefficient dataset contains the unit (comp_code), year (acct_year), month (acct_month), and department (dept_id) dimensions, and the employee workload dataset contains the unit (comp_code), year (acct_year), month (acct_month), department (dept_id), and employee (emp_code) dimensions. It can be seen that the dimensions of the department coefficient dataset are included in the employee workload dataset, which meets the conditions for continuing the calculation.

[0044] In step S106, one of the selected data sets is determined as the first data set to determine the output dimension based on the dimension of the first data set, so that no matter how the dimensions of the multiple data sets involved in the calculation differ, the dimension of the output calculation result data set can be determined. In short, if M[] is used as the first data set, the dimension of the output data set is the same as M[]. In other words, if you want to output which dimensions, you can determine the data set containing (including equal cases) these dimensions as the first data set. For example, if the employee workload data set above is determined as the first data set, the output dimensions are the dimensions contained in the first data set, namely the five dimensions of unit (comp_code), year (acct_year), month (acct_month), department (dept_id), and employee (emp_code).

[0045] In step S108, the data items under each dataset dimension in the selected dataset are uniformly sorted, either in ascending order or in descending order.

[0046] For the data within the dimensions of the first dataset, they are uniformly sorted in ascending or descending order according to the rank of the dimensions contained in the first dataset. In the above example, the employee workload dataset is the first dataset, which contains the dimensions of unit, year, month, department, and individual. The data within these dimensions are uniformly sorted in ascending or descending order, first by unit, then by year, then by month, then by department, and finally by individual. For the same dimensions in other datasets (here, the department coefficient dataset), the data within the corresponding dimensions are uniformly sorted in ascending or descending order according to the rank of the dimensions in the first dataset. No processing is performed on different dimensions.

[0047] In step S110, starting with the first dataset, calculations are performed on a numerical column of each selected dataset in sequence, in the order of dataset size and using a predetermined operation method, based on the principle of minimum dimensional matching, until all selected datasets have been calculated. The operation method can be one or more of the four arithmetic operations: addition, subtraction, multiplication, and division.

[0048] For example, if there are three datasets that can be processed: F[] with 100 data items, N[] with 200 data items, and M[] with 300 data items, and the data size order of the datasets is F[], N[], and M[], and the predetermined operation method is multiplication (*), the calculation is performed based on the data size order and minimum dimensional matching. Minimum dimensional matching means that the dimensions and data volume always match the dimensions and data volume of the first dataset during the calculation process.

[0049] If the first data set is F[], the first calculation is F[]*N[], and the second calculation is the first result*M[];

[0050] If the first data set is M[], the first calculation is M[]*F[], and the second calculation is the first result*N[];

[0051] If the first data set is N[], the first calculation is N[]*F[], and the second calculation is the first result*M[].

[0052] When the two data sets to be operated on each contain more than two numerical columns, the operation is performed on only one numerical column of one data set and the corresponding numerical column of the other data set at a time.

[0053] For example, for the aforementioned analysis scenario of calculating the relative value data of employee workload (employee point value), the expected calculation logic is represented by the following matrix:

[0054]

[0055] When the contents of two datasets correspond one-to-one, perform the numerical operation directly on the corresponding columns. For example, if the contents of datasets A and B below correspond one-to-one and the predetermined operation is multiplication (*), then perform the multiplication of the corresponding columns serve_work and coefficient to obtain dataset C.

[0056] String String String String String Numerical comp_code acct_year acct_month dept_id emp_code serve_work 1001 2021 10 6 100101 200 1001 2021 10 7 100102 300 1001 2021 10 8 100108 50 1001 2021 10 9 100104 10 1001 2021 10 10 100105 26 1002 2021 10 11 100232 56 1002 2021 10 12 100203 84 1002 2021 10 13 100204 102 1002 2021 10 14 100205 216 1002 2021 10 15 100206 36 1003 2021 10 16 100301 53 1003 2021 10 17 100302 79 1003 2021 10 18 100303 55 1003 2021 10 19 100304 69 1003 2021 10 20 100305 107 … … … … … …

[0057] Dataset A

[0058]

[0059]

[0060] Dataset B

[0061] String String String String String Numerical comp_code acct_year acct_month dept_id emp_code value 1001 2021 10 6 100101 200*0.6 1001 2021 10 7 100102 300*5 1001 2021 10 8 100108 50*10 1001 2021 10 9 100104 10*3 1001 2021 10 10 100105 26*0.2 1002 2021 10 11 100232 56*0.8 1002 2021 10 12 100203 84*7 1002 2021 10 13 100204 102*0.3 1002 2021 10 14 100205 216*6 1002 2021 10 15 100206 36*1 1003 2021 10 16 100301 53*0.8 1003 2021 10 17 100302 79*2 1003 2021 10 18 100303 55*0.9 1003 2021 10 19 100304 69*6 1003 2021 10 20 100305 107*5 … … … … … …

[0062] Dataset C

[0063] When the content of two datasets does not correspond one-to-one, the matrix operation is performed on the values of the corresponding columns of the data in the two datasets. For example, the contents of datasets D and E below do not correspond one-to-one. Only the first three data items in dataset E correspond to the contents of dataset D. The predetermined operation method is multiplication (*). The values of the coefficient and serve_work columns of the corresponding data are taken and the matrix operation shown in the following equation is performed to obtain dataset F:

[0064]

[0065] String String String String Numerical comp_code acct_year dept_id code_type coefficient 1001 2021 6 0 15.6

[0066] Dataset D

[0067] String String String String String String Numerical comp_code acct_year acct_month dept_id code_type emp_code serve_work 1001 2021 10 6 0 100101 200 1001 2021 10 6 0 100102 300 1001 2021 10 6 0 100108 50 1001 2021 10 6 1 100104 10 1001 2021 10 6 1 100105 26

[0068] Dataset E

[0069] String String String String Numerical comp_code acct_year dept_id code_type value 1001 2021 6 0 15.6*200+15.6*300+15.6*50

[0070] Dataset F

[0071] In other embodiments, matrix numerical operations may not be used, but calculations may be performed separately.

[0072] When the predetermined operation mode is a division operation, the divisor value column data is converted into 1 / n to perform a matrix operation.

[0073] In step S112, the calculation results for the corresponding analysis scenario are output based on the dimensions of the first dataset and the calculated value columns. For example, in the analysis scenario described above, which calculates the relative value data (employee point values) of employee workload, the employee workload dataset is the first dataset, which contains five dimensions: unit, year, month, department, and employee. The output dataset then contains these five dimensions and the calculated employee point value columns.

[0074] In step S120 , the user is prompted that the selected data set does not meet the calculation conditions and needs to be reselected.

[0075] Figure 2 A block diagram of a preferred embodiment of a multi-dimensional hybrid computing system for enterprise data according to the present invention is shown, the system comprising:

[0076] A selection module 202 is used to select data sets to be calculated based on the analysis scenario;

[0077] A first determining module 204 is configured to determine whether all dimensions of the dataset with the least dimensions in the selected dataset are contained in any other datasets in the selected dataset;

[0078] a second determining module 206 for determining one of the selected data sets as a first data set to determine output dimensions according to the dimensions of the first data set in response to all dimensions of the data set with the least dimensions among the selected data sets being included in any remaining data sets of the selected data sets;

[0079] The sorting module 208 is used to uniformly sort the data items under each dataset dimension in the selected dataset;

[0080] The calculation module 210 is configured to calculate a numerical column of the selected data set starting from the first data set and in accordance with the order of data size of the data sets and a predetermined calculation method;

[0081] The output module 212 is configured to output calculation results of the corresponding analysis scenario according to the dimensions of the first data set and the calculated numerical columns.

[0082] In another embodiment, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the Figure 1 The steps or implementation combinations of the method embodiments or other corresponding method embodiments are shown. Figure 2 The functions of the system embodiment shown and described or other corresponding system embodiments will not be described in detail here.

[0083] In another embodiment, the present invention provides a computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the combined Figure 1 The steps or implementation combinations of the method embodiments or other corresponding method embodiments are shown. Figure 2 The functions of the system embodiment shown and described or other corresponding system embodiments will not be described in detail here.

[0084] The multiple different embodiments described herein or their specific features, structures or characteristics can be appropriately combined in one or more embodiments of the present invention. In addition, in some cases, as long as it is appropriate, the order of steps in the flow chart and / or the pipeline process description can be modified and does not have to be performed in the exact order described. In addition, the multiple different aspects of the present invention can be implemented using software, hardware, firmware or a combination thereof and / or other computer-implemented modules or devices that perform the functions described. The software implementation of the present invention may include executable code stored in a computer-readable medium and executed by one or more processors. The computer-readable medium may include a computer hard drive, ROM, RAM, flash memory, portable computer storage medium such as CD-ROM, DVD-ROM, flash drive and / or other devices with a universal serial bus (USB) interface, and / or any other suitable tangible or non-transient computer-readable medium or executable code can be stored thereon and a computer memory executed by a processor. The present invention can be used in conjunction with any suitable operating system.

[0085] Unless otherwise specified, the singular forms "a", "an", and "the" used herein include the plural meaning (i.e., having "at least one"). It should be further understood that the terms "having", "including", and / or "comprising" used in this specification indicate the presence of the recited features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the listed items.

[0086] While some preferred embodiments of the present invention have been described above, it should be emphasized that the present invention is not limited to these embodiments and may be implemented in other ways within the scope of the present invention. Those skilled in the art may make various variations and modifications to the present invention based on the technical concept of the present invention and without departing from the scope of the present invention, and such variations and modifications shall still fall within the scope of protection of the present invention.

Claims

1. A multi-dimensional hybrid computing method for enterprise data, characterized in that: The method comprises: Select the data sets to be calculated based on the analysis scenario; Determine whether all dimensions of the least dimensional dataset among the selected datasets are contained in any remaining datasets in the selected datasets; In response to dimensions of the least dimensional dataset among the selected datasets being all contained in any remaining datasets of the selected datasets, determining one of the selected datasets as a first dataset to determine output dimensions according to the dimensions of the first dataset; Sort the data items under each dataset dimension in the selected dataset in a unified manner; Starting from the first data set, calculating a numerical column of the selected data set in sequence according to the data size order of the data sets and a predetermined operation method; Outputting calculation results of the corresponding analysis scenario according to the dimensions of the first data set and the calculated numerical columns; The unified sorting includes: For the data under the dimension of the first dataset, arrange them in ascending or descending order according to the rank of the dimensions contained in the dataset; For the same dimension in different data sets, the data under the corresponding dimension are uniformly arranged in ascending or descending order according to the dimension rank in the first data set; No processing is done on the dimensions of the remaining datasets that are different from those of the first dataset; The step of sequentially calculating a numerical column of the selected data set includes: Starting from the first data set, a predetermined operation is performed on a numerical column of the first data set and a corresponding numerical column of the data set with the smallest data size among the remaining selected data sets, and a predetermined operation is performed on the result of the previous operation and a corresponding numerical column of the data set with the smallest data size among the data sets that have not yet participated in the operation among the selected data sets, until all data sets of the selected data sets have completed the operation.

2. The method according to claim 1, characterized in that The step of sequentially calculating a numerical column of the selected data set includes: When the contents of the two data sets correspond one to one, perform numerical operations directly on the corresponding numerical columns; When the contents of the two data sets do not correspond one to one, the values of the numerical columns of the corresponding data corresponding to the contents are taken to perform matrix operations.

3. The method according to claim 1, characterized in that The step of sequentially calculating a numerical column of the selected data set includes: When the two data sets to be operated on each contain more than two numerical columns, the operation is performed on only one numerical column of one data set and the corresponding numerical column of the other data set at a time.

4. The method according to claim 1, wherein The step of sequentially calculating a numerical column of the selected data set includes: When the predetermined operation mode is a division operation, the divisor value sequence data is converted into 1 / n.

5. The method according to claim 1, wherein The candidate data sets include at least one dimension and at least one value column, wherein the dimension positions of different data sets have been sorted according to one or more predefined dimension sorting rules.

6. A multi-dimensional hybrid computing system for enterprise data, characterized in that: The system comprises: The selection module is used to select the data sets to be calculated based on the analysis scenario; a first determining module, configured to determine whether all dimensions of the dataset with the least dimensions among the selected datasets are contained in any remaining datasets of the selected datasets; a second determining module configured to, in response to dimensions of the dataset with the least dimensions among the selected datasets being all included in any remaining datasets of the selected datasets, determine one of the selected datasets as a first dataset to determine output dimensions according to the dimensions of the first dataset; The sorting module is used to uniformly sort the data items under each dataset dimension in the selected dataset; a calculation module, configured to calculate a numerical column of the selected data set in sequence starting from the first data set and according to the order of data size of the data sets and a predetermined calculation method; An output module, configured to output calculation results of a corresponding analysis scenario based on the dimensions of the first data set and the calculated numerical columns; The unified sorting includes: For the data under the dimension of the first dataset, arrange them in ascending or descending order according to the rank of the dimensions contained in the dataset; For the same dimension in different data sets, the data under the corresponding dimension are uniformly arranged in ascending or descending order according to the dimension rank in the first data set; No processing is done on the dimensions of the remaining datasets that are different from those of the first dataset; The step of sequentially calculating a numerical column of the selected data set includes: Starting from the first data set, a predetermined operation is performed on a numerical column of the first data set and a corresponding numerical column of the data set with the smallest data size among the remaining selected data sets, and a predetermined operation is performed on the result of the previous operation and a corresponding numerical column of the data set with the smallest data size among the data sets that have not yet participated in the operation among the selected data sets, until all data sets of the selected data sets have completed the operation.

7. A computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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